Project description:Rapeseed is a critical cash crop globally, and understanding its distribution can assist in refined agricultural management, ensuring a sustainable vegetable oil supply, and informing government decisions. China is the leading consumer and third-largest producer of rapeseed. However, there is a lack of widely available, long-term, and large-scale remotely sensed maps on rapeseed cultivation in China. Here this study utilizes multi-source data such as satellite images, GLDAS environmental variables, land cover maps, and terrain data to create the China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 (CARM30). Our product was validated using independent samples and showed average F1 scores of 0.869 and 0.971 for winter and spring rapeseed. The CARM30 has high spatial consistency with existing 10 m and 20 m rapeseed maps. Additionally, the CARM30-derived rapeseed planted area was significantly correlated with agricultural statistics (R2 = 0.65-0.86; p < 0.001). The obtained rapeseed distribution information can serve as a reference for stakeholders such as farmers, scientific communities, and decision-makers.
Project description:E-JOURNAL LINKED ABSTRACT URL http://www.current-oncology.com/index.php/oncology/article/view/840/ Pseudocirrhosis is a rare form of liver disease that causes clinical symptoms and shows radiographic signs of cirrhosis, but that has histologic features suggesting a distinct pathologic process. In the setting of cancer, hepatic metastases and systemic chemotherapy are suspected causes of pseudocirrhosis. We present the case of a 49-year-old woman with medullary thyroid carcinoma metastatic to the liver who developed pseudocirrhosis. The patient was initially enrolled in a phase i clinical trial of 5-fluorouracil, leucovorin, and oxaliplatin (folfox) in combination with sunitinib (NCT00599924). After this patient’s liver metastases regressed measurably, she was switched to sunitinib maintenance. After 4 months of combination therapy with folfox–sunitinib and 15 months of sunitinib maintenance, she developed abdominal bloating, early satiety, and right upper quadrant pain that increased with inspiration. Computed tomography of the abdomen revealed cirrhotic morphology changes in the liver, including the appearance of a nodular surface and capsular retraction. The patient had no risk factors for cirrhosis and laboratory testing for causes of liver disease were normal or negative. Core-needle liver biopsy demonstrated sheets and nests of epithelioid and spindle cells resembling the primary tumor; septal fibrosis and regenerative nodules typical of cirrhosis were not observed. The background hepatic plate architecture was intact. Laboratory studies showed increased aminotransferases, alkaline phosphatase, and international normalized ratio, and decreased albumin. Portal hypertension, esophageal varices, portal hypertensive gastropathy, and hepatic hydrothorax developed as a result of advanced liver disease. Because of disease progression, sunitinib was discontinued, and the patient was managed with sorafenib. Pseudocirrhosis has often been attributed to chemotherapeutic agents, particularly in the context of metastatic breast cancer. The toxicity profiles of folfox and sunitinib include hepatic steatosis and other forms of hepatotoxicity, but cirrhotic-like disease has not been reported. Considering the transformation of discrete hepatic metastases into a diffuse carcinomatous infiltrate and the unrelated toxicities of folfox and sunitinib, we diagnosed this patient with carcinomatous pseudocirrhosis secondary to metastatic medullary thyroid carcinoma. We discuss the diagnosis of pseudocirrhosis in this case and review the literature regarding pseudocirrhosis in cancer.
Project description:Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rejected directly without search, and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are represented in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. The present proposal is contrasted in several dimensions with neural networks models of associative memory. We discuss the operational characteristics of the entropic associative memory for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence on the potential of this framework for developing practical applications and computational models of natural memory.
Project description:In this article, we employ simple descriptive methods in order to explore the peculiar behavior of the symbols in the Voynich Manuscript. Such an analysis reveals a group of symbols which are further analyzed for the possibility of being compounds (or ligatures), using a specifically developed method. The results suggest the possibility that the alphabet of the manuscript is a lot smaller, and steganographic type of encoding is proposed to explain the newly revealed properties.
Project description:Most palm leaf manuscripts are generally accessible in deteriorated condition, including cracks, discoloration, moisture and humidity, and insects bite. Such a manuscript is considered challenging in the research field. We captured deteriorated Tamil palm leaves around 262 dataset samples are 'Naladiyar(27)',' Tholkappiyam(221)', and' Thirikadugam(14)' which are genned up mortal health, discipline, authoritative text on Tamil grammar. We contribute the high-quality raw dataset with the aid of a Nikon camera, pre-enhance samples by editing software tool, and applied the Otsu threshold to deliver the ground images through binarization as readily accessible content presenting a highly time-consuming task to play a vital role in Machine/Deep/ Transfer learning, AI, and ANN.